Jonathan D. Klingler - Teaching

Home About Research Teaching

I have taught, been scheduled to teach, or have proposed teaching the following courses at Vanderbilt University for the 2017-2018 academic year. Syllabi for each course are available for download via the links below:

Recent Scheduled and Prepared Courses

Introduction to American Politics and Government

(Instructor, Summer 2017, Summer 2013, Summer 2012)

(Teaching Assistant, Spring 2012 with Valeria Sinclair-Chapman)

Syllabus

The currents of American political life are defined by the constraints that enduring political institutions apply to channel evolving public opinion. Much of the tumult in American politics we see today can be understood through consideration of the foundations of American government, major political institutions, and mechanisms that link citizens and government. This course will examine how the government of the United States is organized, the rationale behind its organization, and the ways citizens, political actors, and political institutions interact to achieve political goals. This course is appropriate for political science majors who wish to gain a foundation in American politics as well as for non-majors who simply wish to gain a better understanding of American government and processes.

Survey Research Methods/Quantitative Political Research Methods

(Instructor, Scheduled Spring 2018)

(Teaching Assistant, Fall 2012, Fall 2011, Fall 2010, Spring 2009, with Michael Peress)

Syllabus

This course offers an introduction to the understanding of politics through data analysis, with particular emphasis on surveys of the mass public. We will study selecting a sample, designing and conducting a survey, interpreting the results of a survey, correcting for bias in a survey, and measuring the accuracy of a survey. During the semester, we will pay special attention to the accuracy of public opinion polling preceding the 2012 and 2016 primary and Presidential elections.

Missing Data SPSS File
Machine Learning
Syllabus

Perception and reasoning by intelligent systems requires the processing of inputs into a reasonable mapping of the world, learning from experience, and management of the uncertainty that pervades the world around us. This course will provide a firm introduction to machine learning and the design of intelligent systems capable of handling these three tasks. The course will engage with machine learning by developing the theoretical and mathematical underpinnings of a variety of algorithms, and apply successful algorithms in several contexts to witness their limitations and strengths. This course is appropriate for graduate students and computer science undergraduates comfortable with programming who have basic training in probability theory and familiarity with linear algebra and calculus.

Additional Courses

Probability and Inference; Linear Models (Graduate); Maximum Likelihood Estimation (Graduate) ; Introduction to Python; Text as Data (Graduate)

Introduction to Game Theory; The Presidency ; The Congress; Campaigns and Elections; The Public Presidency (Graduate)

Political Psychology; Political Communication; Personality and Politics (Graduate)